{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/luiza_cohere_com/retrieval-model-safety\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "while Path.cwd().name != 'retrieval-model-safety':\n",
    "    %cd ..\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "sns.set(context=\"paper\", style=\"white\", font_scale=1.5, palette=\"RdBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "base = pd.DataFrame([\n",
    "    {\"model\": \"GPT2 Small\", \"base\": \"GPT2 Small\", \"family\": \"GPT2\", \"EMT\": 0.39, \"Perplexity\": 57.19 , \"Diversity (dist-1)\": 0.61},\n",
    "    {\"model\": \"GPT2 Medium\", \"base\": \"GPT2 Medium\", \"family\": \"GPT2\", \"EMT\": 0.39, \"Perplexity\": 35.94, \"Diversity (dist-1)\": 0.61},\n",
    "    {\"model\": \"GPT2 Large\", \"base\": \"GPT2 Large\", \"family\": \"GPT2\", \"EMT\": 0.39, \"Perplexity\": 24.66 , \"Diversity (dist-1)\": 0.58},\n",
    "    {\"model\": \"Pythia 1B\", \"base\": \"Pythia 1B\", \"family\": \"Pythia\", \"EMT\": 0.38, \"Perplexity\": 44.25, \"Diversity (dist-1)\": 0.59}, \n",
    "    {\"model\": \"Pythia 6.9B\", \"base\": \"Pythia 6.9B\", \"family\": \"Pythia\", \"EMT\": 0.38, \"Perplexity\": 33.93, \"Diversity (dist-1)\": 0.57}, \n",
    "    {\"model\": \"OPT 1.3B\", \"base\": \"OPT 1.3B\", \"family\": \"OPT\", \"EMT\": 0.45, \"Perplexity\": 33.38, \"Diversity (dist-1)\": 0.57}, \n",
    "    {\"model\": \"OPT 6.7B\", \"base\": \"OPT 6.7B\", \"family\": \"OPT\", \"EMT\": 0.45, \"Perplexity\": 30.96, \"Diversity (dist-1)\": 0.56}, \n",
    "])\n",
    "\n",
    "# Goodtriever\n",
    "results = pd.DataFrame([\n",
    "    {\"model\": \"Goodtriever (GPT2 Small)\", \"base\": \"GPT2 Small\", \"family\": \"GPT2\", \"EMT\": 0.20, \"Perplexity\": 32.95, \"Diversity (dist-1)\": 0.57},\n",
    "    {\"model\": \"Goodtriever (GPT2 Medium)\", \"base\": \"GPT2 Medium\", \"family\": \"GPT2\", \"EMT\": 0.22, \"Perplexity\": 23.71, \"Diversity (dist-1)\": 0.57},\n",
    "    {\"model\": \"Goodtriever (GPT2 Large)\", \"base\": \"GPT2 Large\", \"family\": \"GPT2\", \"EMT\": 0.22, \"Perplexity\": 27.11, \"Diversity (dist-1)\": 0.58},\n",
    "    {\"model\": \"Goodtriever (Pythia 1B)\", \"base\": \"Pythia 1B\", \"family\": \"Pythia\", \"EMT\": 0.21, \"Perplexity\": 44.24, \"Diversity (dist-1)\": 0.59},\n",
    "    {\"model\": \"Goodtriever (Pythia 6.9B)\", \"base\": \"Pythia 6.9B\", \"family\": \"Pythia\", \"EMT\": 0.23, \"Perplexity\": 29.22, \"Diversity (dist-1)\": 0.54},\n",
    "    {\"model\": \"Goodtriever (OPT 1.3B)\", \"base\": \"OPT 1.3B\", \"family\": \"OPT\", \"EMT\": 0.34, \"Perplexity\": 21.44, \"Diversity (dist-1)\": 0.53},\n",
    "    {\"model\": \"Goodtriever (OPT 6.7B)\", \"base\": \"OPT 6.7B\", \"family\": \"OPT\", \"EMT\": 0.31, \"Perplexity\": 33.14, \"Diversity (dist-1)\": 0.55},\n",
    "])\n",
    "\n",
    "results = pd.concat([results, base]).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = ['EMT', \"Perplexity\", \"Diversity (dist-1)\"]\n",
    "\n",
    "edited = results.copy()\n",
    "for row, vals in edited.iterrows():\n",
    "    base_model = base.query(\"model == @vals.base\")\n",
    "    for m in metrics:\n",
    "        # print(m, vals.base)\n",
    "        base_metric = base_model[m].values[0]\n",
    "        edited.loc[row, m] = (vals[m] - base_metric) / base_metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>base</th>\n",
       "      <th>family</th>\n",
       "      <th>EMT</th>\n",
       "      <th>Perplexity</th>\n",
       "      <th>Diversity (dist-1)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Goodtriever (GPT2 Small)</td>\n",
       "      <td>GPT2 Small</td>\n",
       "      <td>GPT2</td>\n",
       "      <td>-0.487179</td>\n",
       "      <td>-0.423850</td>\n",
       "      <td>-0.065574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Goodtriever (GPT2 Medium)</td>\n",
       "      <td>GPT2 Medium</td>\n",
       "      <td>GPT2</td>\n",
       "      <td>-0.435897</td>\n",
       "      <td>-0.340289</td>\n",
       "      <td>-0.065574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Goodtriever (GPT2 Large)</td>\n",
       "      <td>GPT2 Large</td>\n",
       "      <td>GPT2</td>\n",
       "      <td>-0.435897</td>\n",
       "      <td>0.099351</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Goodtriever (Pythia 1B)</td>\n",
       "      <td>Pythia 1B</td>\n",
       "      <td>Pythia</td>\n",
       "      <td>-0.447368</td>\n",
       "      <td>-0.000226</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Goodtriever (Pythia 6.9B)</td>\n",
       "      <td>Pythia 6.9B</td>\n",
       "      <td>Pythia</td>\n",
       "      <td>-0.394737</td>\n",
       "      <td>-0.138815</td>\n",
       "      <td>-0.052632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Goodtriever (OPT 1.3B)</td>\n",
       "      <td>OPT 1.3B</td>\n",
       "      <td>OPT</td>\n",
       "      <td>-0.244444</td>\n",
       "      <td>-0.357699</td>\n",
       "      <td>-0.070175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Goodtriever (OPT 6.7B)</td>\n",
       "      <td>OPT 6.7B</td>\n",
       "      <td>OPT</td>\n",
       "      <td>-0.311111</td>\n",
       "      <td>0.070413</td>\n",
       "      <td>-0.017857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GPT2 Small</td>\n",
       "      <td>GPT2 Small</td>\n",
       "      <td>GPT2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GPT2 Medium</td>\n",
       "      <td>GPT2 Medium</td>\n",
       "      <td>GPT2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GPT2 Large</td>\n",
       "      <td>GPT2 Large</td>\n",
       "      <td>GPT2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Pythia 1B</td>\n",
       "      <td>Pythia 1B</td>\n",
       "      <td>Pythia</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pythia 6.9B</td>\n",
       "      <td>Pythia 6.9B</td>\n",
       "      <td>Pythia</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>OPT 1.3B</td>\n",
       "      <td>OPT 1.3B</td>\n",
       "      <td>OPT</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>OPT 6.7B</td>\n",
       "      <td>OPT 6.7B</td>\n",
       "      <td>OPT</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        model         base  family       EMT  Perplexity  \\\n",
       "0    Goodtriever (GPT2 Small)   GPT2 Small    GPT2 -0.487179   -0.423850   \n",
       "1   Goodtriever (GPT2 Medium)  GPT2 Medium    GPT2 -0.435897   -0.340289   \n",
       "2    Goodtriever (GPT2 Large)   GPT2 Large    GPT2 -0.435897    0.099351   \n",
       "3     Goodtriever (Pythia 1B)    Pythia 1B  Pythia -0.447368   -0.000226   \n",
       "4   Goodtriever (Pythia 6.9B)  Pythia 6.9B  Pythia -0.394737   -0.138815   \n",
       "5      Goodtriever (OPT 1.3B)     OPT 1.3B     OPT -0.244444   -0.357699   \n",
       "6      Goodtriever (OPT 6.7B)     OPT 6.7B     OPT -0.311111    0.070413   \n",
       "7                  GPT2 Small   GPT2 Small    GPT2  0.000000    0.000000   \n",
       "8                 GPT2 Medium  GPT2 Medium    GPT2  0.000000    0.000000   \n",
       "9                  GPT2 Large   GPT2 Large    GPT2  0.000000    0.000000   \n",
       "10                  Pythia 1B    Pythia 1B  Pythia  0.000000    0.000000   \n",
       "11                Pythia 6.9B  Pythia 6.9B  Pythia  0.000000    0.000000   \n",
       "12                   OPT 1.3B     OPT 1.3B     OPT  0.000000    0.000000   \n",
       "13                   OPT 6.7B     OPT 6.7B     OPT  0.000000    0.000000   \n",
       "\n",
       "    Diversity (dist-1)  \n",
       "0            -0.065574  \n",
       "1            -0.065574  \n",
       "2             0.000000  \n",
       "3             0.000000  \n",
       "4            -0.052632  \n",
       "5            -0.070175  \n",
       "6            -0.017857  \n",
       "7             0.000000  \n",
       "8             0.000000  \n",
       "9             0.000000  \n",
       "10            0.000000  \n",
       "11            0.000000  \n",
       "12            0.000000  \n",
       "13            0.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edited"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = edited.query(\"model != base\").melt(id_vars=[\"model\", \"base\"], value_vars=metrics)\n",
    "\n",
    "sns.set(context=\"paper\", style=\"white\", font_scale=2, palette=\"RdBu\")\n",
    "g = sns.catplot(\n",
    "    data=temp,\n",
    "    y=\"base\",\n",
    "    x=\"value\",\n",
    "    col=\"variable\",\n",
    "    kind=\"bar\",\n",
    "    sharex=True,\n",
    "    palette=\"tab10\"\n",
    ")\n",
    "\n",
    "g.set_titles(\"{col_name}\")\n",
    "for i, ax in enumerate(g.axes.flat):\n",
    "    ax.axvline(x=0, ymax=1, color=\"black\", linestyle=\"--\")\n",
    "    if i == 0:\n",
    "        ax.set_ylabel(\"Goodtriever applied to\")\n",
    "    if i == 1:\n",
    "        ax.set_xlabel(\"Relative difference to base model\")\n",
    "    else:\n",
    "        ax.set_xlabel(\"\")\n",
    "    ax.grid(axis='x')\n",
    "\n",
    "plt.savefig(\"images/model_families_relative.pdf\", format=\"pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1733.5x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = results.melt(id_vars=[\"model\", \"base\", \"family\"], value_vars=metrics)\n",
    "temp.loc[temp.query(\"base == model\").index, \"Model Type\"] = \"Base Model\"\n",
    "temp.loc[temp.query(\"base != model\").index, \"Model Type\"] = \"Goodtriever\"\n",
    "\n",
    "sns.set(context=\"paper\", style=\"white\", font_scale=2, palette=\"RdBu\")\n",
    "temp = temp.query(\"variable == 'EMT'\")\n",
    "\n",
    "g = sns.catplot(\n",
    "    data=temp,\n",
    "    x=\"base\",\n",
    "    y=\"value\",\n",
    "    hue=\"Model Type\",\n",
    "    col=\"family\",\n",
    "    kind=\"bar\",\n",
    "    sharex=False,\n",
    "    sharey=True,\n",
    "    palette=\"tab10\",\n",
    "    hue_order=[\"Base Model\", \"Goodtriever\"]\n",
    ")\n",
    "g.set_titles(\"\")\n",
    "plt.ylabel(metrics[0])\n",
    "for i, ax in enumerate(g.axes.flat):\n",
    "    if i == 0:\n",
    "        ax.set_ylabel(metrics[0])\n",
    "\n",
    "    labels = ax.get_xticklabels()\n",
    "    family = [l.get_text().split(\" \")[0] for l in labels]\n",
    "    new_labels = [l.get_text().split(\" \")[-1] for l in labels]\n",
    "    ax.set_xticklabels(new_labels)\n",
    "    ax.set_xlabel(family[0])\n",
    "    ax.grid(axis='y')\n",
    "\n",
    "sns.move_legend(g, \"upper center\", ncols=2, fontsize=16, title=\"\")\n",
    "plt.savefig(\"images/model_families_absolute.pdf\", format=\"pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "model_safety",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
